Bayesian

Working Paper

Working Paper abstract Accurate estimation of the prevalence of people with opioid use disorder (OUD) is critical to the success of treatment and resource planning. Various indirect estimation approaches have been used but are subject to issues related to data availability and infrastructure.

abstract The decades-long overdose epidemic in the United States is driven by opioid misuse. Overdoses commonly, although not exclusively, occur in individuals with opioid use disorder (OUD). To allocate adequate resources and develop appropriately scaled public health responses, accurate estimation of the prevalence of OUD is needed.

We propose a two-stage Bayesian hierarchical model with spatial smoothing using a capture-recapture data structure to estimate community-specific opioid use disorder (OUD) prevalence. We explicitly model the hidden prevalence of interest and the associated detection model that describes individual detection histories across data sources.

We used multiple individually linked administrative health data to compare the results of stratified capture-recapture analysis and Bayesian multiplier benchmark analysis to estimate OUD prevalence in the Massachusetts population.

We extend the Bayesian hierarchical modeling framework from efficacy assessment studies in basket trial design to characterize the treatment’s safety profile. We propose Bayesian multi-level count models adopting different likelihood options with additional focus placed on the choice of prior for the basket-level standard deviation.